Lido Dual Governance Model

A modular agent-based model of the Lido Dual Governance system, based on the open-source Solidity implementation.

Implements the official Lido Dual Governance specifications

This model utilizes an open-source Python library radCAD, an extension to cadCAD.

Model Assumptions

The model implements the official Lido Dual Governance Specification wherever possible, and adds additional actor-level assumptions for simulations detailed in the ASSUMPTIONS.md document.

Model Capabilities

The main model capabilities are described in the MODELLING_CAPABILITIES.md document. There you find:

• Current model state
• Parametrized elements of DG
• Available scenario analytics
• Tested scenario templates
• Possible questions to be answered
• Scenarios in the research pipeline

You could either look into already completed simulation notebooks in the folder or run simulations on your local machine via the instructions below.

Environment Setup

1. Clone or download this GitHub repository by running git clone https://github.com/collectif-dao/dg-research.git in your terminal.
2. Set up your development environment using Anaconda virtual environment:

Setting Up an Anaconda Virtual Environment

This guide provides step-by-step instructions on how to set up an Anaconda virtual environment for your project. This documentation assumes that you have Anaconda or Miniconda installed on your system.

1. Install Anaconda or Miniconda

If you haven't already installed Anaconda or Miniconda, you can download and install it from the official website:

• Anaconda
• Miniconda

2. Create a New Virtual Environment

To create a new virtual environment, use the following command:

conda create --name lido-dual-governance-abm

3. Activate the Virtual Environment

To start using the virtual environment, you need to activate it:

conda activate myenv

4. Install Required Packages

Once the environment is activated, you can install the necessary packages. You can install the packages using:

conda install --file requirements.txt

Or alternatively using pip:

pip install -r requirements.txt

Or by using a target from Makefile:

make install

Running Simulations

The Makefile provides a set of targets to automate the execution of different simulations.

To run all the defined simulations in sequence, use the following command:

make run_simulations

This will execute the all existing simulations in order.

Or you can run a specific simulation by it's name, for example:

make model_validation

Development

Current model code has been tested on Python 3.12 version, so please let us know if you're facing issues with older versions.

Testing model and specifications

We use Pytest to test the model code, as well as the notebooks.

To execute the Pytest tests simply run:

pytest

Benchmarking

We use Scalene to benchmark CPU/GPU and Memory consumption. After running a benchmark Scalene will open a Web UI with details on the simulation consumption.

Note

If you're running on Windows you'll be able to see reports only on CPU and GPU profiling, but not memory or copy profiling.

The following command runs Scalene on a provided benchmark.

scalene benchmarks/memory_profiling.py

Model Architecture

WIP: Would be provided in later stage